Using Lookaheads with Optimal Best-First Search

Roni Stern, Tamar Kulberis, Ariel Felner, Robert Holte

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations


We present an algorithm that exploits the complimentary benefits of best-first search (BFS) and depth-first search (DFS) by performing limited DFS lookaheads from the frontier of BFS. We show that this continuum requires significantly less memory than BFS. In addition, a time speedup is also achieved when choosing the lookahead depth correctly. We demonstrate this idea for breadth-first search and for A*. Additionally, we show that when using inconsistent heuristics, Bidirectional Pathmax (BPMX), can be implemented very easily on top of the lookahead phase. Experimental results on several domains demonstrate the benefits of all our ideas.

Original languageEnglish
Title of host publicationProceedings of the 24th AAAI Conference on Artificial Intelligence, AAAI 2010
PublisherAAAI press
Number of pages6
ISBN (Electronic)9781577354642
StatePublished - 15 Jul 2010
Externally publishedYes
Event24th AAAI Conference on Artificial Intelligence, AAAI 2010 - Atlanta, United States
Duration: 11 Jul 201015 Jul 2010

Publication series

NameProceedings of the 24th AAAI Conference on Artificial Intelligence, AAAI 2010


Conference24th AAAI Conference on Artificial Intelligence, AAAI 2010
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2010, Association for the Advancement of Artificial Intelligence ( All rights reserved.


Acknowledgments This research was supported by the Israeli Science Foundation grant no. 305/09 and by the iCORE and NSERC grants. References

FundersFunder number
Natural Sciences and Engineering Research Council of Canada
Israel Science Foundation305/09


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